The presentation below, "Using Bayesian Optimization to Tune Machine Learning Models" by Scott Clark of SigOpt is from MLconf. The talk briefly introduces Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. The talk motivates the problem and gives example applications. Clark also talks about the development of a robust benchmark suite for our algorithms including test selection, metric design, infrastructure architecture, visualization, and comparison to other standard and open source methods. He discusses how this evaluation framework empowers our research engineers to confidently and quickly make changes to our core optimization engine.

Scott Clark has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott was chosen as one of Forbes' 30 under 30 in 2016. Managing Big Data has become a major competitive advantage for many organizations and hence maintaining a proper analytics platform is vital for an organization's survival. This conference provides insights and potential solutions to address Big Data issues from well known experts and thought leaders through panel sessions and open Q&A sessions.